


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class        A     (		lowerCamelCase_	, unittest.TestCase	):







     '''simple docstring'''







     lowerCamelCase :						Tuple            =   PriorTransformer
     lowerCamelCase :						Any            =   """hidden_states"""

     @property
     def 					UpperCAmelCase__       (				self    :							Any):
      _lowercase:       Optional[int]						=				4
      _lowercase:       str						=				8
      _lowercase:       Optional[Any]						=				7

      _lowercase:       Tuple						=				floats_tensor((batch_size, embedding_dim)).to(_UpperCamelCase)

      _lowercase:       str						=				floats_tensor((batch_size, embedding_dim)).to(_UpperCamelCase)
      _lowercase:       List[str]						=				floats_tensor((batch_size, num_embeddings, embedding_dim)).to(_UpperCamelCase)

      return {
          "hidden_states": hidden_states,
          "timestep": 2,
          "proj_embedding": proj_embedding,
          "encoder_hidden_states": encoder_hidden_states,
      }

     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							Dict=0):
      torch.manual_seed(_UpperCamelCase)
      _lowercase:       str						=				4
      _lowercase:       Optional[int]						=				8
      _lowercase:       List[Any]						=				7

      _lowercase:       int						=				torch.randn((batch_size, embedding_dim)).to(_UpperCamelCase)

      _lowercase:       int						=				torch.randn((batch_size, embedding_dim)).to(_UpperCamelCase)
      _lowercase:       List[str]						=				torch.randn((batch_size, num_embeddings, embedding_dim)).to(_UpperCamelCase)

      return {
          "hidden_states": hidden_states,
          "timestep": 2,
          "proj_embedding": proj_embedding,
          "encoder_hidden_states": encoder_hidden_states,
      }

     @property
     def 					UpperCAmelCase__       (				self    :							Dict):
      return (4, 8)

     @property
     def 					UpperCAmelCase__       (				self    :							int):
      return (4, 8)

     def 					UpperCAmelCase__       (				self    :							Any):
      _lowercase:       List[Any]						=				{
          'num_attention_heads': 2,
          'attention_head_dim': 4,
          'num_layers': 2,
          'embedding_dim': 8,
          'num_embeddings': 7,
          'additional_embeddings': 4,
      }
      _lowercase:       Optional[int]						=				self.dummy_input
      return init_dict, inputs_dict

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]):
      _lowercase:       Optional[int]						=				PriorTransformer.from_pretrained(
          "hf-internal-testing/prior-dummy"			,				output_loading_info=_UpperCamelCase)
      self.assertIsNotNone(_UpperCamelCase)
      self.assertEqual(len(loading_info["missing_keys"])			,				0)

      model.to(_UpperCamelCase)
      _lowercase:       str						=				model(**self.dummy_input)[0]

      assert hidden_states is not None, "Make sure output is not None"

     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      _lowercase:       Tuple						=				self.prepare_init_args_and_inputs_for_common()

      _lowercase:       Union[str, Any]						=				self.model_class(**_UpperCamelCase)
      _lowercase:       Optional[Any]						=				inspect.signature(model.forward)
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      _lowercase:       List[Any]						=				[*signature.parameters.keys()]

      _lowercase:       Any						=				['hidden_states', 'timestep']
      self.assertListEqual(arg_names[:2]			,				_UpperCamelCase)






     def 					UpperCAmelCase__       (				self    :							List[str]):
      _lowercase:       List[str]						=				PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
      _lowercase:       List[str]						=				model.to(_UpperCamelCase)

      if hasattr(_UpperCamelCase			,				"set_default_attn_processor"):
       model.set_default_attn_processor()

      _lowercase:       List[Any]						=				self.get_dummy_seed_input()

      with torch.no_grad():
       _lowercase:       int						=				model(**_UpperCamelCase)[0]

      _lowercase:       Optional[int]						=				output[0, :5].flatten().cpu()
      print(_UpperCamelCase)

      # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
      # the expected output slices are not the same for CPU and GPU.
      _lowercase:       Optional[int]						=				torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9])
      self.assertTrue(torch_all_close(_UpperCamelCase			,				_UpperCamelCase			,				rtol=1e-2))






@slow
class        A     (		unittest.TestCase	):







     '''simple docstring'''







     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							Tuple=1			,				_UpperCamelCase    :							Tuple=768			,				_UpperCamelCase    :							List[str]=77			,				_UpperCamelCase    :							Optional[Any]=0):
      torch.manual_seed(_UpperCamelCase)
      _lowercase:       List[Any]						=				batch_size
      _lowercase:       str						=				embedding_dim
      _lowercase:       Dict						=				num_embeddings

      _lowercase:       List[Any]						=				torch.randn((batch_size, embedding_dim)).to(_UpperCamelCase)

      _lowercase:       List[Any]						=				torch.randn((batch_size, embedding_dim)).to(_UpperCamelCase)
      _lowercase:       int						=				torch.randn((batch_size, num_embeddings, embedding_dim)).to(_UpperCamelCase)

      return {
          "hidden_states": hidden_states,
          "timestep": 2,
          "proj_embedding": proj_embedding,
          "encoder_hidden_states": encoder_hidden_states,
      }

     def 					UpperCAmelCase__       (				self    :							str):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






     @parameterized.expand(
         [
             # fmt: off
             [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
             [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
             # fmt: on
         ])
     def 					UpperCAmelCase__       (				self    :							str			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							int):
      _lowercase:       Dict						=				PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"			,				subfolder="prior")
      model.to(_UpperCamelCase)
      _lowercase:       int						=				self.get_dummy_seed_input(seed=_UpperCamelCase)

      with torch.no_grad():
       _lowercase:       str						=				model(**_UpperCamelCase)[0]

      assert list(sample.shape) == [1, 768]

      _lowercase:       Optional[int]						=				sample[0, :8].flatten().cpu()
      print(_UpperCamelCase)
      _lowercase:       str						=				torch.tensor(_UpperCamelCase)

      assert torch_all_close(_UpperCamelCase			,				_UpperCamelCase			,				atol=1e-3)









"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 						__UpperCamelCase     (     UpperCamelCase						):
					@staticmethod
					@abstractmethod
					def        UpperCAmelCase__ (							UpperCAmelCase							:	ArgumentParser   )	->       Optional[Any]:
											raise NotImplementedError()
					@abstractmethod
					def        UpperCAmelCase__ (							self							:	str   )	->       List[str]:
											raise NotImplementedError()